Anomalous Behavior Research Articles

Hierarchically Coupled Ornstein–Uhlenbeck Processes for Transient Anomalous Diffusion

The nonlinear dependence of the mean-squared displacement (MSD) on time is a common characteristic of particle transport in complex environments. Frequently, this anomalous behavior only occurs transiently before the particle reaches a terminal Fickian diffusion. This study shows that a system of hierarchically coupled Ornstein–Uhlenbeck equations is able to describe both transient subdiffusion and transient superdiffusion dynamics, as well as their sequential combinations. To validate the model, five distinct experimental, molecular dynamics simulation, and theoretical studies are successfully described by the model. The comparison includes the transport of particles in random optical fields, supercooled liquids, bedrock, soft colloidal suspensions, and phonons in solids. The model’s broad applicability makes it a convenient tool for interpreting the MSD profiles of particles exhibiting transient anomalous diffusion.

A multiscale electricity theft detection model based on feature engineering

With the widespread adoption of smart meters and the growing availability of data mining and machine learning algorithms, there is a pressing demand for methods that are both accurate and explicable in identifying electricity theft patterns among end-users. To address this need, this study proposes a multi-scale anomaly detection model based on feature engineering.Specifically, tsfresh is utilized in feature engineering to extract electricity consumption features from the raw data, and XGBoost is employed to select features that are highly correlated with anomalous behavior, which have clear physical interpretations. Multi-scale convolutional neural networks are then used to analyze and process the data at different temporal and frequency scales. Attention mechanisms are applied to assign weights to different feature channels, and all of the extracted information is fused for anomaly detection. The combination of feature engineering and multi-scale convolutional neural networks not only enhances the interpretability of the model but also improves its performance, as demonstrated by the experimental results, which show that the proposed method outperforms traditional anomaly detection approaches across multiple evaluation metrics.

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

Hydrogeochemical changes with emphasis on trace elements in an artesian well before and after the September 8, 2018 Ms 5.9 Mojiang Earthquake in Yunnan, southwest China

Most groundwater geochemical studies associated with earthquakes have focused on changes in major ions and radon concentration. However, trace elements have also shown a dramatic response to earthquakes and attracted increasing attention. In this study, hydrochemical and water level changes were detected by an artesian well (DZ well) in Yunnan, China, during the 2018 Ms 5.9 Mojiang earthquake. The results show that the major and trace element concentrations changed considerably before and after the earthquake, which were attributed to the mixing of water from different aquifers due to the earthquake-induced alteration of permeability through wavelet analysis of continuous monitoring data and coseismic static strain. In addition, Self-Organizing Map (SOM) and K-means clustering algorithms were used to classify all water samples with 10 trace elements as variables, and the results showed that samples collected in pre- and post-earthquake can be categorized as two groups, showing a significant difference. We argue that the response of trace elements remains unaffected by their initial concentration levels. Notably, both high- and low-concentration trace elements (e.g., Cs, Rb, V, etc.) exhibit discernible changes in response to the Ms 5.9 Mojiang earthquake. These observations corroborate the efficacy of clustering methodologies in delineating seismic-induced concentration shifts and furnish fresh insights into the anomalous behavior of trace elements during the earthquake.

Methodological refinement of the submillimeter galaxy magnification bias. I. Cosmological analysis with a single redshift bin

The main goal of this work is to test the results of a methodological improvement in the measurement of the magnification bias signal on a sample of submillimeter galaxies. In particular, we investigate the constraining power of cosmological parameters within the Lambda CDM model. We also discuss important points that can affect the results. We measured the angular cross-correlation function between a sample of foreground GAMA II galaxies in a single wide spectroscopic redshift bin of $0.2<z<0.8$ and a sample of background submillimeter galaxies from Herschel-ATLAS. We focused on the photometric redshift range of $1.2<z<4.0,$ with an improved methodological framework. Interpreting the weak lensing signal within the halo model formalism and performing a Markov chain Monte Carlo (MCMC) algorithm, we obtained the posterior distribution of both the halo occupation distribution and cosmological parameters within a flat Lambda CDM model. Our analysis was also performed with additional galaxy clustering information via a foreground angular auto-correlation function. We observed an overall remarkable improvement in terms of uncertainties in both the halo occupation distribution and cosmological parameters with respect to previous results. A priori knowledge about beta , the logarithmic slope of the background integral number counts, is found to be paramount to derive constraints on $ when using the cross-correlation data alone. Assuming a physically motivated prior distribution for beta , we obtain mean values of $ $ and $ $ and an unconstrained distribution for the Hubble constant. These results are likely to suffer from sampling variance, since one of the fields, G15, appears to have an anomalous behavior with a systematically higher cross-correlation. We find that removing it from the sample yields mean values of $ $ and $ $ and, for the first time, a (loose) restriction of the Hubble constant is obtained via this observable: $h=0.79^ $. The addition of the angular auto-correlation of the foreground sample in a joint analysis tightens the constraints, but also reveals a discrepancy between both observables that might be an aggravated consequence of sampling variance or due to the presence of unmodeled aspects on small and intermediate scales.

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

Ultralow Glassy Thermal Conductivity and Controllable, Promising Thermoelectric Properties in Crystalline o-CsCu5S3.

We thoroughly investigated the anharmonic lattice dynamics and microscopic mechanisms of the thermal and electronic transport characteristics in orthorhombic o-CsCu5S3 at the atomic level. Taking into account the phonon energy shifts and the wave-like tunneling phonon channel, we predict an ultralow κL of 0.42 w/mK at 300 K with an extremely weak temperature dependence following ∼T-0.33. These findings agree well with experimental values along with the parallel to the Bridgman growth direction. The κL in o-CsCu5S3 is suppressed down to the amorphous limit, primarily due to the unconventional Cu-S bonding induced by the p-d hybridization antibonding state coupled with the stochastic oscillation of Cs atoms. The nonstandard temperature dependence of κL can be traced back to the critical or dominant role of wave-like tunneling of phonon contributions in thermal transport. Moreover, the p-d hybridization of Cu(3)-S bonding results in the formation of a valence band with "pudding-mold" and high-degeneracy valleys, ensuring highly efficient electron transport characteristics. By properly adjusting the carrier concentration, excellent thermoelectric performance is achieved with a maximum thermoelectric conversion efficiency of 18.4% observed at 800 K in p-type o-CsCu5S3. Our work not only elucidates the anomalous electronic and thermal transport behavior in the copper-based chalcogenide o-CsCu5S3 but also provides insights for manipulating its thermal and electronic properties for potential thermoelectric applications.

Anomalous Detachment Behavior and Directional Reconstruction Regulation of Leaching-Type Precatalysts for Industrial Water Electrolyzers.

Current reconstruction chemistry studies are mainly operated at the laboratory scale, where the operating parameters are different from those used in industrial water electrolyzers. This gap leads to unclear reconstruction behaviors under industrial conditions and constrains the application of catalysts. Here, this work presents a new reconstruction mechanism and anomalous detachment phenomena observed in leaching-type oxygen-evolving precatalysts under industrial conditions, different from the reported results obtained under laboratory conditions. The identified detachment issues are closely linked to the production of a potassium salt separate phase, which proves sensitive to the local environment, and its instability easily leads to catalyst stripping from the substrate. By establishing detachment critical point and operating parameter-detachment correlation, a targeted reconstruction strategy is proposed to achieve smooth ligand leaching and effectively solve the detachment issue. Theoretical analyses validate the dual-site regulation in directionally reconstructed catalysts with optimized intermediate adsorption. Under industrial conditions, the coupled electrolyzer delivers an industrial-level current density at low cell voltage with prolonged durability, 1 A cm-2 at 2 V for over 340 h. This work bridges the gap of leaching-type precatalysts between laboratory test conditions and industrial operating conditions.

Electrical and dielectric characterization of Ge quantum dots embedded in MIS structure (AuPd/SiO2:Ge QDs/n-Si) grown by MBE

Monocrystalline Germanium quantum dots (Ge QDs), grown by molecular beam epitaxy (MBE), are intricately embedded within a SiO2 oxide layer of a metal-insulator-semiconductor (MIS) structure, catering to optoelectronic applications. These monocrystalline Ge QDs exhibit high density (D ∼ 5 x 1012 cm−2), possess a spherical morphology, and an average size of 6 nm with narrow size distribution. A comprehensive investigation into the influence of these quantum dots on the electrical transport and dielectric properties of the metal-insulator-semiconductor (MIS) structure was conducted through current-voltage (I–V) spectroscopy and impedance spectroscopy. Notably, I–V measurements demonstrate that Ge QDs enhance conduction phenomena, thereby improving electrical transport within the MIS structure. Furthermore, the capacitance-voltage (C–V) and conductance-voltage (G-V) measurements, spanning the inversion and accumulation regions, demonstrate a memory effect induced by Ge QDs within the MOS structure. Extensive analysis of dielectric parameters, including permittivity (ε′), dielectric loss (ε”), dissipation factor (tanδ), as well as electrical modulus (M’ and M”), across a broad frequency range and under varying bias voltages, reveals no anomalous electrical or dielectric behavior attributable to the presence of Ge QDs in MIS structure. Consequently, these findings affirm the viability of the novel MIS structures with Ge QDs for the development of optoelectronic devices such as photodetectors or solar cells.

Dielectric Response of Different Alcohols in Water-Rich Binary Mixtures from THz Ellipsometry.

We report a study on the hydrogen bonding mechanisms of three aliphatic alcohols (2-propanol, methanol, and ethanol) and one diol (ethylene glycol) in water solution using a time-domain ellipsometer in the THz region. The dielectric response of the pure liquids is nicely modeled by the generalized Debye-Lorentz equation. For binary mixtures, we analyze the data using a modified effective Debye model, which considers H-bond rupture and reformation dynamics and the motion of the alkyl chains and of the OH groups. We focus on the properties of the water-rich region, finding anomalous behavior in the absorption properties at very low solute molar concentrations. These results, first observed in the THz region, are in line with previous findings from different experiments and can be explained by taking into account the amphiphilic nature of the alcohol molecules.

A Unified Model for Non-Fickian Diffusion and Anomalous Swelling of Glassy Polymer Gels.

A sheet of glassy polymers placed in a solvent shows swelling behaviors quite different from that of soft polymers (rubbers and gels). (1) Non-Fickian diffusion (called case II diffusion): As solvent permeates into the sample, a sharp front is created between the swollen part and the glassy part, and it moves toward the center at constant speed. (2) Nonmonotonous swelling: The thickness of the sample first increases and then decreases toward the equilibrium value. Here we propose a theory to explain such anomalous behavior by extending the previous theory for swelling of soft gels. We regard the material as a continuum mixture of a glassy polymer network and solvent. We assume that the polymer network is a viscoelastic gel of glassy polymers, and its relaxation time depends strongly on solvent concentration. We show that this theory explains the above two characteristics of glassy polymers in a simple and unified framework. The theory predicts how the permeation speed of the solvent and the characteristic times of the swelling process depend on material parameters and experimental conditions, which can be checked experimentally.

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

Spin-reorientation driven emergent phases and unconventional magnetotransport in quasi-2D vdW ferromagnet Fe4GeTe2

Non-trivial spin textures driven by strong exchange interaction, magneto-crystalline anisotropy, and electron correlation in a low-dimensional magnetic material often lead to unusual electronic transitions. Through a combination of transport experiments in exfoliated nanoflakes down to 16 layers and first principle calculations, we unravel emergent electronic phases in quasi-2D van der Waals ferromagnet, Fe4GeTe2, possessing ferromagnetic TC ~ 270 K, along with a spin-reorientation transition (TSR ~ 120 K) with the change of magnetic easy axis. Two electronic transitions are identified. The first transition near TSR exhibits a sharp fall in resistivity, followed by a sign change in the ordinary Hall coefficient (R0), together with, maximum negative magnetoresistance (MR) and anomalous Hall conductivity. Another unusual electronic transition, hitherto unknown, is observed near ~ 40–50 K (TQ), where R0 again changes sign and below which, the resistivity shows a quadratic temperature dependence, and MR becomes positive. An analysis of the experimental data further uncovers the role of competing inelastic scattering processes in anomalous magnetotransport behavior. The density-functional theory based first-principle calculations unveil two possible magnetic phases, followed by a low-energy model Hamiltonian which captures the essence of these phases as well as explains the observed magnetotransport behavior. Thus, we demonstrate an interplay between magnetism and band topology and its consequence on electron transport in Fe4GeTe2, important for spintronic applications.

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.
 The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.
 The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.
 In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.

Anomalous behavior in entanglement speed profile through spin chains.

The origin of the uniform Dzyaloshinskii-Moriya interaction (DMI), which is responsible for the creation of chiral magnetism, has been the subject of extensive research. Recently, modern technology has allowed for its production and utilization in a modulated form. Not only can magnetic phases of spin chains be enriched by the presence of such a potential as detailed in Japaridze etal. [Phys. Rev. E 104, 014134 (2021)10.1103/PhysRevE.104.014134], but the capacity of such systems for information transmission is also greatly enhanced. The current paper examines the impact of a staggered pattern of DMI (STDMI) on a chain with a substrate XX Heisenberg interaction. It is demonstrated how enhancing the intensity of this coupling improves the propagation of an entangled quantum state. Additionally, as our analysis has shown, the initial condition over the system's state has a profound effect on the speed at which entanglement spreads. The aberrant behavior of the entanglement's speed profile in response to fine-tuning of the phase factor which adjusts the initial state is the focus of this paper. This anomalous behavior is characterized by dramatic drops in speed for certain phase factor values. We have also shown that, using wave interference principles, we can predict exactly why these phenomena occur. This research will pave the way for additional studies on STDMI and its potential applications in the field of quantum information.

Anomalous deformation behavior of Ag nanowires on Au electrode in low-temperature environments

We discovered that Ag nanowires (AgNWs) on an Au electrode exhibited an anomalous deformation behavior despite a low-temperature environment of 65 °C. Most AgNWs on the Au electrode were deformed after heating. In contrast, AgNWs on the Cr and Ag electrodes exhibited a few changes and maintained their initial shape. The deformation behavior of AgNWs on metal electrodes has not yet been reported and is currently difficult to explain using known processes such as diffusion and alloying. Nonetheless, they evidently depend on the electrode material. The findings of this study are crucial for the design of AgNW-based electronic devices.

Anomalous behaviour of the ionic conductivity of nanoconfined IL -lithium salt mixtures

The effect of salt addition on the protic ionic liquid (IL) ethylimidazolium nitrate is analysed in this work in terms of structural changes measured by NMR, thermal behaviour and ionic conductivity. Additionally, the effect of the immobilization of these mixtures in nano-silica structures on the same properties is also studied. Results show that the influence of salt on the chemical shifts of the signals associated to hydrogen atoms in the apolar regions of the ionic liquid is almost negligible, both in liquid and gel states. The salt accommodates in the polar region of the IL, according to the nanostructured solvation paradigm. The liquid range increases with salt concentration in both states, being this effect especially interesting for high and low temperature applications. The ionic conductivity takes appropriate values to use these compounds as electrolytes in electrochemical devices; pure IL presents 6.9 mS·cm−1 at 298 K rising to 17.38 mS·cm−1 at 323 K and this property decreases with salt concentration in the liquid state, as can be expected. Although gel samples present lower ionic conductivity than liquid samples, an anomalous behaviour can be observed in the ionogel samples upon the salt concentration, characterized by an absolute maximum, 13.26 mS·cm−1 at 323 K, at the lowest salt concentration (0.5 mol·kg−1 of Li+ salt addition), which represents the 12 % higher than the corresponding values of pure ionogel at this temperature.

Anomalous Pressure Diffusion and Deformation in Two‐ and Three‐Dimensional Heterogeneous Fractured Media

AbstractIn fractured and stress‐sensitive reservoirs and aquifers, hydromechanical coupling is important, in connection with their heat and solute transport properties, and because the fluid production or extraction leads to land subsidence and potentially to induced seismicity. Classical dual‐porosity poroelasticity (DPP) models cannot upscale pressure diffusion and deformation in fractured porous media, which are characterized by anomalous behaviors that manifest in strong tailing in the temporal evolution of flow rate and subsidence. We study these behaviors using detailed numerical simulations of fluid production in naturally fractured formations characterized by multi‐Gaussian distributions of the matrix permeability. We find that the tailing behaviors depend on the permeability contrast between fracture and matrix, on the permeability distribution in the matrix, and on the correlation length. We use a non–equilibrium, multi‐porosity model to quantify the coupled behaviors of anomalous pressure diffusion, fluid flow and deformation. The model is parameterized by medium and fluid properties, which set the characteristic pressure diffusion time scales. It allows to identify the emerging scaling regimes and scaling behaviors of flow rate and subsidence. We propose a model implementation that captures the full anomalous evolution of flow rates and displacements observed in the detailed numerical simulations in terms of the permeability distribution and matrix length scales. The presented results shed new light on the controls of medium heterogeneity and geometry on pressure diffusion, fluid production and subsidence in highly heterogeneous fractured media.

Distinguishing between fractional Brownian motion with random and constant Hurst exponent using sample autocovariance-based statistics.

Fractional Brownian motion (FBM) is a canonical model for describing dynamics in various complex systems. It is characterized by the Hurst exponent, which is responsible for the correlation between FBM increments, its self-similarity property, and anomalous diffusion behavior. However, recent research indicates that the classical model may be insufficient in describing experimental observations when the anomalous diffusion exponent varies from trajectory to trajectory. As a result, modifications of the classical FBM have been considered in the literature, with a natural extension being the FBM with a random Hurst exponent. In this paper, we discuss the problem of distinguishing between two models: (i) FBM with the constant Hurst exponent and (ii) FBM with random Hurst exponent, by analyzing the probabilistic properties of statistics represented by the quadratic forms. These statistics have recently found application in Gaussian processes and have proven to serve as efficient tools for hypothesis testing. Here, we examine two statistics-the sample autocovariance function and the empirical anomaly measure-utilizing the correlation properties of the considered models. Based on these statistics, we introduce a testing procedure to differentiate between the two models. We present analytical and simulation results considering the two-point and beta distributions as exemplary distributions of the random Hurst exponent. Finally, to demonstrate the utility of the presented methodology, we analyze real-world datasets from the financial market and single particle tracking experiment in biological gels.

Correlations between Ca ii H and K Emission and the Gaia M Dwarf Gap

The Gaia M dwarf gap, also known as the Jao Gap, is a novel feature discovered in the Gaia Data Release 2 G versus BP-RP color–magnitude diagram. This gap represents a 17% decrease in stellar density in a thin magnitude band around the convective transition mass (∼0.35 M ⊙) on the main sequence. Previous work has demonstrated a paucity of Hα emission coincident with the G magnitude of the Jao Gap in the solar neighborhood. The exact mechanism that results in this paucity is as of yet unknown; however, the authors of the originating paper suggest that it may be the result of complex variations to a star’s magnetic topology driven by the Jao Gap’s characteristic formation and breakdown of stars’ radiative transition zones. We present a follow-up investigating another widely used magnetic activity metric, Calcium ii H and K emission. Ca ii H and K activity appears to share a similar anomalous behavior as Hα does near the Jao Gap magnitude. We observe an increase in star-to-star variation of magnetic activity near the Jao Gap. We present a toy model of a star’s magnetic field evolution, which demonstrates that this increase may be due to stochastic disruptions to the magnetic field originating from the periodic-mixing events characteristic of the convective kissing instabilities that drive the formation of the Jao Gap.